Barry Gough, Possessing Meares Island A Historian’s Journey into the Past of Clayoquot Sound by William S. Dudley
Bibliographic record
Abstract
In his latest book, Canadian historian Barry Gough brings us face to face with historical origins of modern environmental problems, involving the rights of Indigenous inhabitants of ancient lands as opposed to interests of private corporations, provinces, and the nation of Canada.Another theme running through his text is the impact of modern industries on forested environment and native habitats, as private and governmental interests exploit nature's bounty and convert it to profitable and popular uses.The over-hunting of species to the point of extinction, such as the sea otter, and the clear-cutting of primeval forests that cannot be replaced are but two examples, as Gough shows in this fascinating study.As explorers from foreign shores ventured to places such as Meares Island, British Columbia, the Indigenous inhabitants showed a willingness to enable the exploiters, whether they were from New England, Great Britain, Russia, or Spain.The trade in sea otter pelts, fish oil, and timber commenced during the eighteenth century.After Captain Cook's discovery of the west coast of Vancouver Island, merchants from New England and Europe were quick to react, sending ships to participate in the sea otter trade with China.Royal Navy commander John Meares found the island now named for him in Clayoquot Sound.Over the next two centuries, the migration of settlers to the Pacific Coast brought an increasing exploitation of these natural species and raw materials to the point where the native inhabitants realized they were helping to deprive themselves of the ancient habitat, religion, and customs on which they depended.Several small tribes of First Nations peoples inhabited Vancouver Island at the time the European traders arrived.Of primary interest here are those of Meares Island, the Nuu-Chah-Nulth, and others of the Clayoquot, and Ahousaht-Kelsemaht nations.Over time, the steady incursion of logging corporations had made in-roads, to the point that in 1984, local tribal representatives, environmental activists, and First Nations lawyers gathered to resist any further logging of the old growth forest on Meares Island.In writing the history of Meares Island, Gough tells his own story of how a law firm asked him to participate in a legal suit as an expert witness on the history of Vancouver Island and its First Nations peoples.He and his anthropologist colleagues had three years to complete their research and to "make sure no stone is unturned."Gough raises the question and meaning of "possession" as he used the
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".